---
title: "Report"
date: "`r Sys.Date()`"
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    social: menu
    source_code: embed
---

```{r setup, include=FALSE}
## Global options
library(reticulate)
library(knitr)
library(flexdashboard)
library(scales)
library(here)
library(tidyverse)
library(readr)
library(plotly)
library(tidyverse)
knitr::opts_chunk$set(cache = TRUE)
source(here('modules','data_funcs.R'))

# PARAMS
warm <- 250
dur <- 1000
repl <- 30
path <- here('data','full-sm')



# NO CUU - OR * 1
modif <- '0-1'
dt_pl_n10 <- generate_summary(path, modif, dur, warm, repl)
dt_sa_n10 <- generate_summary_sa(path, modif, dur, warm, repl)
dt_zs_n10 <- generate_summary_zs(path, modif, FALSE)

# NO CUU - OR * 1.1
modif <- '0-1.1'
dt_pl_n11 <- generate_summary(path, modif, dur, warm, repl)
dt_sa_n11 <- generate_summary_sa(path, modif, dur, warm, repl)
dt_zs_n11 <- generate_summary_zs(path, modif, FALSE)

# NO CUU - OR * 1.2
modif <- '0-1.2'
dt_pl_n12 <- generate_summary(path, modif, dur, warm, repl)
dt_sa_n12 <- generate_summary_sa(path, modif, dur, warm, repl)
dt_zs_n12 <- generate_summary_zs(path, modif, FALSE)

# NO CUU - OR * 1.3
modif <- '0-1.3'
dt_pl_n13 <- generate_summary(path, modif, dur, warm, repl)
dt_sa_n13 <- generate_summary_sa(path, modif, dur, warm, repl)
dt_zs_n13 <- generate_summary_zs(path, modif, FALSE)



# CUU - OR * 1
modif <- '1000-1'
dt_pl_y10 <- generate_summary(path, modif, dur, warm, repl)
dt_sa_y10 <- generate_summary_sa(path, modif, dur, warm, repl)
dt_zs_y10 <- generate_summary_zs(path, modif, TRUE)

# CUU - OR * 1.1
modif <- '1000-1.1'
dt_pl_y11 <- generate_summary(path, modif, dur, warm, repl)
dt_sa_y11 <- generate_summary_sa(path, modif, dur, warm, repl)
dt_zs_y11 <- generate_summary_zs(path, modif, TRUE)

# CUU - OR * 1.2
modif <- '1000-1.2'
dt_pl_y12 <- generate_summary(path, modif, dur, warm, repl)
dt_sa_y12 <- generate_summary_sa(path, modif, dur, warm, repl)
dt_zs_y12 <- generate_summary_zs(path, modif, TRUE)

# CUU - OR * 1.3
modif <- '1000-1.3'
dt_pl_y13 <- generate_summary(path, modif, dur, warm, repl)
dt_sa_y13 <- generate_summary_sa(path, modif, dur, warm, repl)
dt_zs_y13 <- generate_summary_zs(path, modif, TRUE)



# MODEL DATA SUMMARY
arrival_rate <- data.frame(
  Surgery = c('Surgery 1', 'Surgery 1', 'Surgery 4', 
              'Surgery 4', 'Surgery 6', 'Surgery 6'),
  Complexity = c('Complexity 1', 'Complexity 2', 'Complexity 1', 
                 'Complexity 2', 'Complexity 1', 'Complexity 2'),
  "Arrival_Adjusted" = c(1.23, 0.62, 0.14, 0.10, 1.23, 0.62),
  "Arrival_Original" = c(1, 0.5, 0.0833, 0.0625, 1, 0.5), 
  Rationale = c("once per week", "once per two weeks", "once per 3 months", 
                "once per 4 months", "once per week", "once per 2 weeks")
)

resource_usage <- data.frame(
  Surgery = c('Surgery 1', 'Surgery 1', 'Surgery 1', 'Surgery 1', 
              'Surgery 4', 'Surgery 4', 'Surgery 4', 'Surgery 4', 
              'Surgery 6', 'Surgery 6', 'Surgery 6', 'Surgery 6'),
  Complexity = c('Complexity 1', 'Complexity 1', 'Complexity 2', 
                 'Complexity 2', 'Complexity 1', 'Complexity 1', 
                 'Complexity 2', 'Complexity 2', 'Complexity 1', 
                 'Complexity 1', 'Complexity 2', 'Complexity 2'),
  Resource_Type = c('Admissions', 'OR_Time','Admissions', 'OR_Time',
                    'Admissions', 'OR_Time','Admissions', 'OR_Time',
                    'Admissions', 'OR_Time','Admissions', 'OR_Time'), 
  Usage = c(0,3,1,4,1,4,1,5.5,0,1.5,0,2.5)
)

resource_capacity <- data.frame(
  Resouce = c('Admissions', 'OR_Time'),
  Capacity_Weekly = c(1.5, 11.25),
  Unit = c("Patients Admitted per week", "OR Hours per week")
)
```

Model Parameters
=======================================================================

Row
-----------------------------------------------------------------------

### Model Parameters
**Model Instance**

* Planning horizon is decreased from 24 weeks to 10 weeks
* Maximum tracked wait is decreased from 6 weeks to 4 weeks
* There are 3 surgeries instead of 6 surgeries
* Number of priorities is set to 1

**Simulation Parameters**

* 30 Replications
* 1000 weeks duration
* 250 weeks warm up

**Surgeries**

* Surgery 1 - 1. SPINE POSTERIOR DECOMPRESSION/LAMINECTOMY LUMBAR
* Surgery 4 - 4. SPINE POST CERV DECOMPRESSION AND FUSION W INSTR
* Surgery 6 - 6. SPINE POSTERIOR DISCECTOMY LUMBAR

### Arrival Rate
It was set to be 95% of the capacity, however due to transitions, the resource usage should be higher than 95%
``` {r echo=FALSE, cache=FALSE}
kable(arrival_rate)
```

Row
-----------------------------------------------------------------------

### Resource Usage
```{r echo=FALSE, cache=FALSE}
kable(resource_usage)
```

### Resource Capacity
```{r echo=FALSE, cache=FALSE}
kable(resource_capacity)
```





NP, 1
=======================================================================

Row {data-height=650}
-----------------------------------------------------------------------

### Policy Description
The policy description is based on a graph to the right

* The left column shows MDP policy, the right column shows Myopic Policy
* The top row shows scheduling costs, adjusted for resource usage, the bottom is not adjusted
* The bottom row shows in which days the policy will allow scheduling.
* The top row shows approximate order of patient scheduling.

Additionally the far right graph shows the approximate decision making 

### Policy Math Graph

```{r echo=FALSE}
dt_zs_n10$zf_plt %>% ggplotly()
```

### Policy Evidence Graph

```{r echo=FALSE}
dt_sa_n10$res_plot$sched_plt %>% ggplotly()
```

Row 
-----------------------------------------------------------------------

### Wait Times in weeks
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_n10$results$pw)
```

### Wait List Size
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_n10$results$wtl)
```

### Transitions per week
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_n10$results$tr)
```

### Utilization
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_n10$results$util)
```

Row
-----------------------------------------------------------------------

### Reschedules
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_n10$results$rsc)
```

### Wait List Size by Group
```{r echo=FALSE, cache=FALSE}
dt_sa_n10$res_plot$waitlist_plt %>% ggplotly()
```

### Reschedules by Group
```{r echo=FALSE, cache=FALSE}
dt_sa_n10$res_plot$rsc_plt %>% ggplotly()
```





NP, 1.1
=======================================================================

Row {data-height=650}
-----------------------------------------------------------------------

### Policy Description
The policy description is based on a graph to the right

* The left column shows MDP policy, the right column shows Myopic Policy
* The top row shows scheduling costs, adjusted for resource usage, the bottom is not adjusted
* The bottom row shows in which days the policy will allow scheduling.
* The top row shows approximate order of patient scheduling.

Additionally the far right graph shows the approximate decision making 

### Policy Math Graph

```{r echo=FALSE}
dt_zs_n11$zf_plt %>% ggplotly()
```

### Policy Evidence Graph

```{r echo=FALSE}
dt_sa_n11$res_plot$sched_plt %>% ggplotly()
```

Row 
-----------------------------------------------------------------------

### Wait Times in weeks
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_n11$results$pw)
```

### Wait List Size
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_n11$results$wtl)
```

### Transitions per week
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_n11$results$tr)
```

### Utilization
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_n11$results$util)
```

Row
-----------------------------------------------------------------------

### Reschedules
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_n11$results$rsc)
```

### Wait List Size by Group
```{r echo=FALSE, cache=FALSE}
dt_sa_n11$res_plot$waitlist_plt %>% ggplotly()
```

### Reschedules by Group
```{r echo=FALSE, cache=FALSE}
dt_sa_n11$res_plot$rsc_plt %>% ggplotly()
```





NP, 1.2
=======================================================================

Row {data-height=650}
-----------------------------------------------------------------------

### Policy Description
The policy description is based on a graph to the right

* The left column shows MDP policy, the right column shows Myopic Policy
* The top row shows scheduling costs, adjusted for resource usage, the bottom is not adjusted
* The bottom row shows in which days the policy will allow scheduling.
* The top row shows approximate order of patient scheduling.

Additionally the far right graph shows the approximate decision making 

### Policy Math Graph

```{r echo=FALSE}
dt_zs_n12$zf_plt %>% ggplotly()
```

### Policy Evidence Graph

```{r echo=FALSE}
dt_sa_n12$res_plot$sched_plt %>% ggplotly()
```

Row 
-----------------------------------------------------------------------

### Wait Times in weeks
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_n12$results$pw)
```

### Wait List Size
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_n12$results$wtl)
```

### Transitions per week
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_n12$results$tr)
```

### Utilization
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_n12$results$util)
```

Row
-----------------------------------------------------------------------

### Reschedules
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_n12$results$rsc)
```

### Wait List Size by Group
```{r echo=FALSE, cache=FALSE}
dt_sa_n12$res_plot$waitlist_plt %>% ggplotly()
```

### Reschedules by Group
```{r echo=FALSE, cache=FALSE}
dt_sa_n12$res_plot$rsc_plt %>% ggplotly()
```





NP, 1.3
=======================================================================

Row {data-height=650}
-----------------------------------------------------------------------

### Policy Description
The policy description is based on a graph to the right

* The left column shows MDP policy, the right column shows Myopic Policy
* The top row shows scheduling costs, adjusted for resource usage, the bottom is not adjusted
* The bottom row shows in which days the policy will allow scheduling.
* The top row shows approximate order of patient scheduling.

Additionally the far right graph shows the approximate decision making 

### Policy Math Graph

```{r echo=FALSE}
dt_zs_n13$zf_plt %>% ggplotly()
```

### Policy Evidence Graph

```{r echo=FALSE}
dt_sa_n13$res_plot$sched_plt %>% ggplotly()
```

Row 
-----------------------------------------------------------------------

### Wait Times in weeks
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_n13$results$pw)
```

### Wait List Size
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_n13$results$wtl)
```

### Transitions per week
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_n13$results$tr)
```

### Utilization
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_n13$results$util)
```

Row
-----------------------------------------------------------------------

### Reschedules
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_n13$results$rsc)
```

### Wait List Size by Group
```{r echo=FALSE, cache=FALSE}
dt_sa_n13$res_plot$waitlist_plt %>% ggplotly()
```

### Reschedules by Group
```{r echo=FALSE, cache=FALSE}
dt_sa_n13$res_plot$rsc_plt %>% ggplotly()
```





P, 1
=======================================================================

Row {data-height=650}
-----------------------------------------------------------------------

### Policy Description
The policy description is based on a graph to the right

* The left column shows MDP policy, the right column shows Myopic Policy
* The top row shows scheduling costs, adjusted for resource usage, the bottom is not adjusted
* The bottom row shows in which days the policy will allow scheduling.
* The top row shows approximate order of patient scheduling.

Additionally the far right graph shows the approximate decision making 

### Policy Math Graph

```{r echo=FALSE}
dt_zs_y10$zf_plt %>% ggplotly()
```

### Policy Evidence Graph

```{r echo=FALSE}
dt_sa_y10$res_plot$sched_plt %>% ggplotly()
```

Row 
-----------------------------------------------------------------------

### Wait Times in weeks
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_y10$results$pw)
```

### Wait List Size
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_y10$results$wtl)
```

### Transitions per week
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_y10$results$tr)
```

### Utilization
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_y10$results$util)
```

Row
-----------------------------------------------------------------------

### Reschedules
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_y10$results$rsc)
```

### Wait List Size by Group
```{r echo=FALSE, cache=FALSE}
dt_sa_y10$res_plot$waitlist_plt %>% ggplotly()
```

### Reschedules by Group
```{r echo=FALSE, cache=FALSE}
dt_sa_y10$res_plot$rsc_plt %>% ggplotly()
```





P, 1.1
=======================================================================

Row {data-height=650}
-----------------------------------------------------------------------

### Policy Description
The policy description is based on a graph to the right

* The left column shows MDP policy, the right column shows Myopic Policy
* The top row shows scheduling costs, adjusted for resource usage, the bottom is not adjusted
* The bottom row shows in which days the policy will allow scheduling.
* The top row shows approximate order of patient scheduling.

Additionally the far right graph shows the approximate decision making 

### Policy Math Graph

```{r echo=FALSE}
dt_zs_y11$zf_plt %>% ggplotly()
```

### Policy Evidence Graph

```{r echo=FALSE}
dt_sa_y11$res_plot$sched_plt %>% ggplotly()
```

Row 
-----------------------------------------------------------------------

### Wait Times in weeks
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_y11$results$pw)
```

### Wait List Size
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_y11$results$wtl)
```

### Transitions per week
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_y11$results$tr)
```

### Utilization
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_y11$results$util)
```

Row
-----------------------------------------------------------------------

### Reschedules
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_y11$results$rsc)
```

### Wait List Size by Group
```{r echo=FALSE, cache=FALSE}
dt_sa_y11$res_plot$waitlist_plt %>% ggplotly()
```

### Reschedules by Group
```{r echo=FALSE, cache=FALSE}
dt_sa_y11$res_plot$rsc_plt %>% ggplotly()
```





P, 1.2
=======================================================================

Row {data-height=650}
-----------------------------------------------------------------------

### Policy Description
The policy description is based on a graph to the right

* The left column shows MDP policy, the right column shows Myopic Policy
* The top row shows scheduling costs, adjusted for resource usage, the bottom is not adjusted
* The bottom row shows in which days the policy will allow scheduling.
* The top row shows approximate order of patient scheduling.

Additionally the far right graph shows the approximate decision making 

### Policy Math Graph

```{r echo=FALSE}
dt_zs_y12$zf_plt %>% ggplotly()
```

### Policy Evidence Graph

```{r echo=FALSE}
dt_sa_y12$res_plot$sched_plt %>% ggplotly()
```

Row 
-----------------------------------------------------------------------

### Wait Times in weeks
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_y12$results$pw)
```

### Wait List Size
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_y12$results$wtl)
```

### Transitions per week
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_y12$results$tr)
```

### Utilization
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_y12$results$util)
```

Row
-----------------------------------------------------------------------

### Reschedules
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_y12$results$rsc)
```

### Wait List Size by Group
```{r echo=FALSE, cache=FALSE}
dt_sa_y12$res_plot$waitlist_plt %>% ggplotly()
```

### Reschedules by Group
```{r echo=FALSE, cache=FALSE}
dt_sa_y12$res_plot$rsc_plt %>% ggplotly()
```





P, 1.3
=======================================================================

Row {data-height=650}
-----------------------------------------------------------------------

### Policy Description
The policy description is based on a graph to the right

* The left column shows MDP policy, the right column shows Myopic Policy
* The top row shows scheduling costs, adjusted for resource usage, the bottom is not adjusted
* The bottom row shows in which days the policy will allow scheduling.
* The top row shows approximate order of patient scheduling.

Additionally the far right graph shows the approximate decision making 

### Policy Math Graph

```{r echo=FALSE}
dt_zs_y13$zf_plt %>% ggplotly()
```

### Policy Evidence Graph

```{r echo=FALSE}
dt_sa_y13$res_plot$sched_plt %>% ggplotly()
```

Row 
-----------------------------------------------------------------------

### Wait Times in weeks
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_y13$results$pw)
```

### Wait List Size
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_y13$results$wtl)
```

### Transitions per week
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_y13$results$tr)
```

### Utilization
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_y13$results$util)
```

Row
-----------------------------------------------------------------------

### Reschedules
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_y13$results$rsc)
```

### Wait List Size by Group
```{r echo=FALSE, cache=FALSE}
dt_sa_y13$res_plot$waitlist_plt %>% ggplotly()
```

### Reschedules by Group
```{r echo=FALSE, cache=FALSE}
dt_sa_y13$res_plot$rsc_plt %>% ggplotly()
```